DoS attack and intrusion prediction is essential for IoT networks because it guarantees service availability, and preserves reputation by reducing expensive downtime. Benign, infiltration traffic, DoS golden eye, hulk, slowloris, and slow http test classes were all not taken at the same time with high accuracy in earlier studies on DoS attack and intrusion detection. This research presents a stacking augmented explainable machine learning method for predicting multiclass DoS assaults and network intrusions. The relevant dataset was gathered from the Kaggle source and verified by cyber specialists. Chi-square, RFE, and mutual information are used in feature selection along with SMOTE-based data balance and preprocessing. SHAP is utilized for model explain ability, while grid search CV is used for hyper parameter tweaking. The best DoS attack and intrusion prediction model selection is examined using RF, XGBoost, catboost, LGBM, LR, and ensemble stacking technique (RF, XGB, LR). The stacking enriched ensemble strategy is chosen as the best prediction model in this paper with an accuracy of 97.953 percent. The accuracy of the suggested method is more than 1.75 percent higher than that of previous studies.
Maimuna Tabassum, Umme Saima Tonny, Sabila Anzum et al.· International Conference on...· 0 citations
Recent advances in large language models (LLMs) and vision transformers have enabled multimodal systems that integrate clinical text with medical imaging for diagnostic decision-making. While these systems show promising results on benchmark datasets in well-resourced research settings, their applicability in low-resource healthcare environments where diagnostic disparities are most severe remains limited and poorly understood. This mini review synthesizes key developments in LLM–vision fusion architectures from 2018 to 2026, with a focus on radiology-oriented visual question answering (VQA) and report generation systems viewed from a deployment perspective. Rather than comprehensively cataloguing multimodal medical AI, we synthesize the evolution of LLM–vision fusion architectures and discuss complementary deployment-enabling strategies, including parameter-efficient adaptation, post-training quantization, federated learning, and multilingual support, where they directly improve the feasibility of radiology AI in resource-constrained healthcare settings. Rather than focusing solely on performance benchmarks, we examine these approaches through a deployment-oriented lens, highlighting trade-offs between representational capacity, computational efficiency, interpretability, and memory footprint. We argue that current progress remains substantially shaped by model scaling and benchmark optimization, which often do not address the memory, connectivity, and annotation constraints of low-resource healthcare systems. While cross-modal transformer architectures provide strong representational alignment, their computational demands and reliance on large curated datasets limit real-world deployment. In contrast, emerging directions including parameter-efficient fine-tuning, post-training quantization, federated learning, and modular agent-based systems offer more tractable pathways toward clinical integration under hardware and data constraints. To bridge the gap between benchmark performance and clinical utility, we identify concrete challenges in data scarcity, multilingual coverage, and calibration, and propose a shift toward lightweight, interpretable, and hardware-aware multimodal AI. This perspective highlights the need to move beyond scaling-centric design toward models that can run on 4–8 GB VRAM, operate offline, and generalize across languages and imaging equipment.
Kahakashan Ashraf, Md.Hamid Hosen, N. Farah et al.· Frontiers in Digital Health· 0 citations
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